def main_vgg(argv):
# dataset = keras.datasets.cifar10
scale = 1
if len(argv) >= 3:
scale = float(argv[2])
(x_train, y_train), (x_test, y_test) = dataprocessing.preprocess(
scale=scale) # dataset.load_data()
mean, std = meanStd(x_train.astype('float32'), x_test.astype('float32'))
x_train = normalize(x_train.astype('float32'), mean, std)
x_test = normalize(x_test.astype('float32'), mean, std)
# y_train = keras.utils.to_categorical(y_train, num_classes=10)
# y_test = keras.utils.to_categorical(y_test, num_classes=10)
datagen = ImageDataGenerator(featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=15,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
vertical_flip=False)
datagen.fit(x_train)
model = VGG(argv[1])
lr = 0.01
lr_decay = 1e-6
lr_drop = 20
def lr_scheduler(epoch):
return lr * (0.5**(epoch // lr_drop))
reduce_lr = keras.callbacks.LearningRateScheduler(lr_scheduler)
sgd = SGD(lr=lr, decay=lr_decay, momentum=0.9, nesterov=True)
model_cp = keras.callbacks.ModelCheckpoint("Model/{0}.model".format(
argv[1]))
model.compile(optimizer=sgd,
loss='binary_crossentropy',
metrics=['accuracy'])
print(model.summary())
batch_size = 128
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
steps_per_epoch=x_train.shape[0] // batch_size,
epochs=int(argv[2]),
validation_data=(x_test, y_test),
callbacks=[reduce_lr, model_cp])
print(model.evaluate(x_test, y_test))
model.save("Model/{0}.model".format(argv[1]))
model.save_weights("Model/{0}.weights".format(argv[1]))
validation_data=(X_valid, Y_valid))
checkpoint_callback = CheckpointCallback(monitor='val_accuracy',
verbose=verbose,
save_weights_only=False)
lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
cooldown=0,
patience=15,
min_lr=0.5e-6)
callbacks = [metrics_callback, checkpoint_callback, lr_reducer]
# set up the architecture
model = VGG(model_type='D',
dropout=0.5,
num_classes=nb_classes,
input_shape=(img_rows, img_cols, img_channels))
# set up the optimizer
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
# Model Summary
print(model.summary())
# start training
model.fit_generator(generator.flow(X_train, Y_train, batch_size=batch_size),
steps_per_epoch=X_train.shape[0] // batch_size + 1,
nb_epoch=nb_epoch,
callbacks=callbacks,
validation_data=(X_valid, Y_valid),
verbose=1)